Automation and evaluation of graduated dot maps

Abstract

ABSTRACTDot mapping is a traditional method for visualizing quantitative data, but current automated dot mapping techniques are limited. The most common automated method places dots pseudo-randomly within enumeration areas, which can result in overlapping dots and very dense dot clusters for areas with large values. These issues affect users’ ability to estimate values. Graduated dot maps use dots with different sizes that represent different values. With graduated dot maps the number of dots on a map is smaller, reducing the likelihood of overlapping dots. This research introduces an automated method of generating graduated dot maps that arranges dots with blue-noise patterns to avoid overlap and uses clustering algorithms to replace densely packed dots with those of larger sizes. A user study comparing graduated dot maps, pseudo-random dot maps, blue-noise dot maps and proportional circle maps with almost 300 participants was conducted. Results indicate that map users can more accurately extract values from graduated dot maps than from the other map types. This is likely due to the smaller number of dots per enumeration area in graduated dot maps. Map users also appear to prefer graduated dot maps over other map types.